Overview

Dataset statistics

Number of variables25
Number of observations5000
Missing cells6661
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory780.9 B

Variable types

Text2
Categorical13
Numeric9
Boolean1

Alerts

customer_lifecycle_finished_flag has constant value "True"Constant
baby_age_first_order is highly overall correlated with baby_age_last_order and 2 other fieldsHigh correlation
baby_age_last_order is highly overall correlated with baby_age_first_order and 1 other fieldsHigh correlation
baby_residual_lifecycle is highly overall correlated with baby_age_first_order and 2 other fieldsHigh correlation
cltv is highly overall correlated with customer_lifecycle and 1 other fieldsHigh correlation
customer_lifecycle is highly overall correlated with cltv and 2 other fieldsHigh correlation
first_order_channel is highly overall correlated with first_order_channel_grp and 1 other fieldsHigh correlation
first_order_channel_grp is highly overall correlated with first_order_channel and 1 other fieldsHigh correlation
first_order_jar is highly overall correlated with order_conversion_jarHigh correlation
first_repurchase_interval is highly overall correlated with customer_lifecycle and 1 other fieldsHigh correlation
frisomum is highly overall correlated with stage_f0High correlation
gold is highly overall correlated with order_conversion_jar and 2 other fieldsHigh correlation
last_repurchase_interval is highly overall correlated with cltv and 2 other fieldsHigh correlation
order_conversion_jar is highly overall correlated with first_order_jar and 1 other fieldsHigh correlation
prestige is highly overall correlated with gold and 1 other fieldsHigh correlation
reg_channel is highly overall correlated with first_order_channel and 4 other fieldsHigh correlation
reg_channel_grp is highly overall correlated with reg_channelHigh correlation
stage_f0 is highly overall correlated with frisomumHigh correlation
stage_f1 is highly overall correlated with stage_f2High correlation
stage_f2 is highly overall correlated with stage_f1High correlation
stage_f3 is highly overall correlated with baby_age_first_order and 1 other fieldsHigh correlation
reg_channel is highly imbalanced (54.8%)Imbalance
first_order_channel is highly imbalanced (87.0%)Imbalance
frisomum is highly imbalanced (89.5%)Imbalance
stage_f0 is highly imbalanced (89.5%)Imbalance
stage_f3 is highly imbalanced (75.8%)Imbalance
stage_f4 is highly imbalanced (98.3%)Imbalance
first_order_channel_grp is highly imbalanced (72.9%)Imbalance
reg_channel_grp is highly imbalanced (50.1%)Imbalance
first_repurchase_interval has 3552 (71.0%) missing valuesMissing
last_repurchase_interval has 2963 (59.3%) missing valuesMissing
first_order_province has 119 (2.4%) missing valuesMissing
baby_age_first_order is highly skewed (γ1 = 25.07499143)Skewed
baby_age_last_order is highly skewed (γ1 = 25.41472605)Skewed
dac_id has unique valuesUnique
baby_age_first_order has 399 (8.0%) zerosZeros
baby_age_last_order has 329 (6.6%) zerosZeros
first_repurchase_interval has 51 (1.0%) zerosZeros
last_repurchase_interval has 626 (12.5%) zerosZeros

Reproduction

Analysis started2026-01-12 05:58:52.165034
Analysis finished2026-01-12 05:59:06.489767
Duration14.32 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

dac_id
Text

UNIQUE 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size493.2 KiB
2026-01-12T13:59:06.599394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters180000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5000 ?
Unique (%)100.0%

Sample

1st row787e290e-d683-11e8-8187-022fee61b306
2nd row5b636287-d684-11e8-8187-022fee61b306
3rd rowd2e74f16-d683-11e8-8187-022fee61b306
4th row5faaacca-d684-11e8-8187-022fee61b306
5th rowbb2c7df4-d684-11e8-8187-022fee61b306
ValueCountFrequency (%)
787e290e-d683-11e8-8187-022fee61b306 1
 
< 0.1%
711597a5-d683-11e8-8187-022fee61b306 1
 
< 0.1%
bb2c7df4-d684-11e8-8187-022fee61b306 1
 
< 0.1%
127b1090-d684-11e8-8187-022fee61b306 1
 
< 0.1%
192eb3e8-d684-11e8-8187-022fee61b306 1
 
< 0.1%
84896107-d683-11e8-8187-022fee61b306 1
 
< 0.1%
852f779a-d683-11e8-8187-022fee61b306 1
 
< 0.1%
f9c64f4c-d683-11e8-8187-022fee61b306 1
 
< 0.1%
8efc79ac-d683-11e8-8187-022fee61b306 1
 
< 0.1%
bf6963e5-d683-11e8-8187-022fee61b306 1
 
< 0.1%
Other values (4990) 4990
99.8%
2026-01-12T13:59:06.945748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 22653
12.6%
1 22345
12.4%
- 20000
11.1%
6 17542
9.7%
e 17427
9.7%
2 12366
6.9%
0 12323
6.8%
3 10049
 
5.6%
d 7698
 
4.3%
b 7674
 
4.3%
Other values (7) 29923
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 22653
12.6%
1 22345
12.4%
- 20000
11.1%
6 17542
9.7%
e 17427
9.7%
2 12366
6.9%
0 12323
6.8%
3 10049
 
5.6%
d 7698
 
4.3%
b 7674
 
4.3%
Other values (7) 29923
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 22653
12.6%
1 22345
12.4%
- 20000
11.1%
6 17542
9.7%
e 17427
9.7%
2 12366
6.9%
0 12323
6.8%
3 10049
 
5.6%
d 7698
 
4.3%
b 7674
 
4.3%
Other values (7) 29923
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 22653
12.6%
1 22345
12.4%
- 20000
11.1%
6 17542
9.7%
e 17427
9.7%
2 12366
6.9%
0 12323
6.8%
3 10049
 
5.6%
d 7698
 
4.3%
b 7674
 
4.3%
Other values (7) 29923
16.6%

reg_channel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size515.0 KiB
线下
2682 
CRM系统
1146 
品牌微信
994 
天猫旗舰店
 
103
京东商城
 
38
Other values (8)
 
37

Length

Max length6
Median length2
Mean length3.1712
Min length2

Characters and Unicode

Total characters15856
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row线下
2nd rowCRM系统
3rd rowCRM系统
4th row线下
5th row品牌微信

Common Values

ValueCountFrequency (%)
线下 2682
53.6%
CRM系统 1146
22.9%
品牌微信 994
 
19.9%
天猫旗舰店 103
 
2.1%
京东商城 38
 
0.8%
美囤 12
 
0.2%
KA合作伙伴 7
 
0.1%
官方网站 7
 
0.1%
其他 4
 
0.1%
蜜芽 3
 
0.1%
Other values (3) 4
 
0.1%

Length

2026-01-12T13:59:07.117841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
线下 2682
53.6%
crm系统 1146
22.9%
品牌微信 994
 
19.9%
天猫旗舰店 103
 
2.1%
京东商城 38
 
0.8%
美囤 12
 
0.2%
ka合作伙伴 7
 
0.1%
官方网站 7
 
0.1%
其他 4
 
0.1%
蜜芽 3
 
0.1%
Other values (3) 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
线 2682
16.9%
2682
16.9%
C 1146
7.2%
R 1146
7.2%
M 1146
7.2%
1146
7.2%
1146
7.2%
994
 
6.3%
994
 
6.3%
994
 
6.3%
Other values (33) 1780
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
线 2682
16.9%
2682
16.9%
C 1146
7.2%
R 1146
7.2%
M 1146
7.2%
1146
7.2%
1146
7.2%
994
 
6.3%
994
 
6.3%
994
 
6.3%
Other values (33) 1780
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
线 2682
16.9%
2682
16.9%
C 1146
7.2%
R 1146
7.2%
M 1146
7.2%
1146
7.2%
1146
7.2%
994
 
6.3%
994
 
6.3%
994
 
6.3%
Other values (33) 1780
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
线 2682
16.9%
2682
16.9%
C 1146
7.2%
R 1146
7.2%
M 1146
7.2%
1146
7.2%
1146
7.2%
994
 
6.3%
994
 
6.3%
994
 
6.3%
Other values (33) 1780
11.2%

baby_residual_lifecycle
Real number (ℝ)

HIGH CORRELATION 

Distinct773
Distinct (%)15.5%
Missing19
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1290.9661
Minimum39
Maximum2900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2026-01-12T13:59:07.270028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile1088
Q11187
median1284
Q31401
95-th percentile1544
Maximum2900
Range2861
Interquartile range (IQR)214

Descriptive statistics

Standard deviation186.23757
Coefficient of variation (CV)0.14426218
Kurtosis10.506228
Mean1290.9661
Median Absolute Deviation (MAD)107
Skewness-0.05022263
Sum6430302
Variance34684.434
MonotonicityNot monotonic
2026-01-12T13:59:07.504729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248 25
 
0.5%
1156 23
 
0.5%
1368 23
 
0.5%
1247 22
 
0.4%
1217 22
 
0.4%
1338 22
 
0.4%
1218 21
 
0.4%
1214 20
 
0.4%
1192 20
 
0.4%
1243 20
 
0.4%
Other values (763) 4763
95.3%
ValueCountFrequency (%)
39 1
< 0.1%
66 1
< 0.1%
144 1
< 0.1%
207 1
< 0.1%
313 1
< 0.1%
314 1
< 0.1%
336 1
< 0.1%
362 1
< 0.1%
363 1
< 0.1%
364 1
< 0.1%
ValueCountFrequency (%)
2900 1
< 0.1%
2827 1
< 0.1%
2816 1
< 0.1%
2690 1
< 0.1%
2666 1
< 0.1%
2647 1
< 0.1%
2620 1
< 0.1%
2561 1
< 0.1%
2455 1
< 0.1%
2400 1
< 0.1%

baby_age_first_order
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct82
Distinct (%)1.6%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.8476781
Minimum-47
Maximum581
Zeros399
Zeros (%)8.0%
Negative582
Negative (%)11.6%
Memory size78.1 KiB
2026-01-12T13:59:07.729902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-47
5-th percentile-3
Q11
median5
Q38
95-th percentile12
Maximum581
Range628
Interquartile range (IQR)7

Descriptive statistics

Standard deviation19.783417
Coefficient of variation (CV)3.3831235
Kurtosis707.56422
Mean5.8476781
Median Absolute Deviation (MAD)4
Skewness25.074991
Sum29215
Variance391.3836
MonotonicityNot monotonic
2026-01-12T13:59:07.911357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 418
 
8.4%
0 399
 
8.0%
8 393
 
7.9%
9 386
 
7.7%
6 365
 
7.3%
2 336
 
6.7%
4 330
 
6.6%
10 314
 
6.3%
3 313
 
6.3%
5 307
 
6.1%
Other values (72) 1435
28.7%
ValueCountFrequency (%)
-47 1
 
< 0.1%
-36 1
 
< 0.1%
-31 1
 
< 0.1%
-29 2
< 0.1%
-27 1
 
< 0.1%
-25 3
0.1%
-24 1
 
< 0.1%
-23 1
 
< 0.1%
-22 3
0.1%
-21 4
0.1%
ValueCountFrequency (%)
581 1
< 0.1%
580 1
< 0.1%
577 1
< 0.1%
573 1
< 0.1%
572 1
< 0.1%
336 1
< 0.1%
128 1
< 0.1%
96 1
< 0.1%
80 1
< 0.1%
71 1
< 0.1%

baby_age_last_order
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct78
Distinct (%)1.6%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.1857486
Minimum-47
Maximum581
Zeros329
Zeros (%)6.6%
Negative492
Negative (%)9.8%
Memory size78.1 KiB
2026-01-12T13:59:08.085676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-47
5-th percentile-2
Q12
median6
Q310
95-th percentile18
Maximum581
Range628
Interquartile range (IQR)8

Descriptive statistics

Standard deviation19.407684
Coefficient of variation (CV)2.7008576
Kurtosis740.40612
Mean7.1857486
Median Absolute Deviation (MAD)4
Skewness25.414726
Sum35900
Variance376.65818
MonotonicityNot monotonic
2026-01-12T13:59:08.259433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 377
 
7.5%
8 377
 
7.5%
11 360
 
7.2%
9 359
 
7.2%
0 329
 
6.6%
6 320
 
6.4%
10 319
 
6.4%
5 312
 
6.2%
2 279
 
5.6%
4 276
 
5.5%
Other values (68) 1688
33.8%
ValueCountFrequency (%)
-47 1
 
< 0.1%
-25 1
 
< 0.1%
-24 2
 
< 0.1%
-22 2
 
< 0.1%
-21 2
 
< 0.1%
-19 3
0.1%
-17 2
 
< 0.1%
-16 5
0.1%
-15 2
 
< 0.1%
-14 1
 
< 0.1%
ValueCountFrequency (%)
581 1
< 0.1%
580 1
< 0.1%
577 1
< 0.1%
573 1
< 0.1%
572 1
< 0.1%
128 1
< 0.1%
96 1
< 0.1%
80 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%

first_order_channel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size540.0 KiB
线下门店
4768 
天猫
 
143
京东
 
57
其他电商
 
28
苏宁
 
3

Length

Max length4
Median length4
Mean length3.9186
Min length2

Characters and Unicode

Total characters19593
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row线下门店
2nd row线下门店
3rd row线下门店
4th row线下门店
5th row线下门店

Common Values

ValueCountFrequency (%)
线下门店 4768
95.4%
天猫 143
 
2.9%
京东 57
 
1.1%
其他电商 28
 
0.6%
苏宁 3
 
0.1%
一号店 1
 
< 0.1%

Length

2026-01-12T13:59:08.436980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:08.602444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
线下门店 4768
95.4%
天猫 143
 
2.9%
京东 57
 
1.1%
其他电商 28
 
0.6%
苏宁 3
 
0.1%
一号店 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4769
24.3%
线 4768
24.3%
4768
24.3%
4768
24.3%
143
 
0.7%
143
 
0.7%
57
 
0.3%
57
 
0.3%
28
 
0.1%
28
 
0.1%
Other values (6) 64
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4769
24.3%
线 4768
24.3%
4768
24.3%
4768
24.3%
143
 
0.7%
143
 
0.7%
57
 
0.3%
57
 
0.3%
28
 
0.1%
28
 
0.1%
Other values (6) 64
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4769
24.3%
线 4768
24.3%
4768
24.3%
4768
24.3%
143
 
0.7%
143
 
0.7%
57
 
0.3%
57
 
0.3%
28
 
0.1%
28
 
0.1%
Other values (6) 64
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4769
24.3%
线 4768
24.3%
4768
24.3%
4768
24.3%
143
 
0.7%
143
 
0.7%
57
 
0.3%
57
 
0.3%
28
 
0.1%
28
 
0.1%
Other values (6) 64
 
0.3%

first_order_jar
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8208
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2026-01-12T13:59:08.739635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum96
Range95
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.993686
Coefficient of variation (CV)3.2917871
Kurtosis133.23539
Mean1.8208
Median Absolute Deviation (MAD)0
Skewness10.874617
Sum9104
Variance35.924272
MonotonicityNot monotonic
2026-01-12T13:59:08.868576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 4638
92.8%
2 166
 
3.3%
12 72
 
1.4%
6 22
 
0.4%
24 22
 
0.4%
4 19
 
0.4%
72 15
 
0.3%
3 11
 
0.2%
48 8
 
0.2%
36 7
 
0.1%
Other values (9) 20
 
0.4%
ValueCountFrequency (%)
1 4638
92.8%
2 166
 
3.3%
3 11
 
0.2%
4 19
 
0.4%
5 4
 
0.1%
6 22
 
0.4%
7 1
 
< 0.1%
8 4
 
0.1%
10 1
 
< 0.1%
12 72
 
1.4%
ValueCountFrequency (%)
96 5
 
0.1%
84 1
 
< 0.1%
72 15
 
0.3%
60 2
 
< 0.1%
48 8
 
0.2%
36 7
 
0.1%
24 22
 
0.4%
18 1
 
< 0.1%
14 1
 
< 0.1%
12 72
1.4%

order_conversion_jar
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.745186
Minimum0.51
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2026-01-12T13:59:09.005027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile0.95
Q11
median1
Q31
95-th percentile2
Maximum96
Range95.49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.5477936
Coefficient of variation (CV)3.1789125
Kurtosis134.59935
Mean1.745186
Median Absolute Deviation (MAD)0
Skewness10.889736
Sum8725.93
Variance30.778014
MonotonicityNot monotonic
2026-01-12T13:59:09.153704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3584
71.7%
0.95 846
 
16.9%
0.9 151
 
3.0%
2 103
 
2.1%
1.02 41
 
0.8%
12 41
 
0.8%
1.07 36
 
0.7%
12.84 24
 
0.5%
4 16
 
0.3%
24 15
 
0.3%
Other values (52) 143
 
2.9%
ValueCountFrequency (%)
0.51 7
 
0.1%
0.59 2
 
< 0.1%
0.74 1
 
< 0.1%
0.83 2
 
< 0.1%
0.85 8
 
0.2%
0.9 151
 
3.0%
0.95 846
 
16.9%
0.96 1
 
< 0.1%
1 3584
71.7%
1.02 41
 
0.8%
ValueCountFrequency (%)
96 2
 
< 0.1%
85.2 2
 
< 0.1%
72 10
0.2%
71.4 1
 
< 0.1%
68.4 1
 
< 0.1%
61.2 2
 
< 0.1%
60 2
 
< 0.1%
56.64 1
 
< 0.1%
52.32 1
 
< 0.1%
48 5
0.1%

prestige
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
0
3448 
1
1552 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3448
69.0%
1 1552
31.0%

Length

2026-01-12T13:59:09.285949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:09.379345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3448
69.0%
1 1552
31.0%

Most occurring characters

ValueCountFrequency (%)
0 3448
69.0%
1 1552
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3448
69.0%
1 1552
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3448
69.0%
1 1552
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3448
69.0%
1 1552
31.0%

frisomum
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
0
4931 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Length

2026-01-12T13:59:09.486427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:09.579885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

gold
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
1
3385 
0
1615 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3385
67.7%
0 1615
32.3%

Length

2026-01-12T13:59:09.681903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:09.779759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3385
67.7%
0 1615
32.3%

Most occurring characters

ValueCountFrequency (%)
1 3385
67.7%
0 1615
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3385
67.7%
0 1615
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3385
67.7%
0 1615
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3385
67.7%
0 1615
32.3%

stage_f0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
0
4931 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Length

2026-01-12T13:59:09.882158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:09.978457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

stage_f1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
0
3065 
1
1935 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3065
61.3%
1 1935
38.7%

Length

2026-01-12T13:59:10.222250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:10.320396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3065
61.3%
1 1935
38.7%

Most occurring characters

ValueCountFrequency (%)
0 3065
61.3%
1 1935
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3065
61.3%
1 1935
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3065
61.3%
1 1935
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3065
61.3%
1 1935
38.7%

stage_f2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
1
2815 
0
2185 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 2815
56.3%
0 2185
43.7%

Length

2026-01-12T13:59:10.422790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:10.520351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2815
56.3%
0 2185
43.7%

Most occurring characters

ValueCountFrequency (%)
1 2815
56.3%
0 2185
43.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2815
56.3%
0 2185
43.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2815
56.3%
0 2185
43.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2815
56.3%
0 2185
43.7%

stage_f3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
0
4800 
1
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
96.0%
1 200
 
4.0%

Length

2026-01-12T13:59:10.626895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:10.724926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
96.0%
1 200
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 4800
96.0%
1 200
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
96.0%
1 200
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
96.0%
1 200
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
96.0%
1 200
 
4.0%

stage_f4
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.3 KiB
0
4992 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4992
99.8%
1 8
 
0.2%

Length

2026-01-12T13:59:10.833782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:10.941554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4992
99.8%
1 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 4992
99.8%
1 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4992
99.8%
1 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4992
99.8%
1 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4992
99.8%
1 8
 
0.2%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
True
5000 
ValueCountFrequency (%)
True 5000
100.0%
2026-01-12T13:59:11.028938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

first_repurchase_interval
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct369
Distinct (%)25.5%
Missing3552
Missing (%)71.0%
Infinite0
Infinite (%)0.0%
Mean102.11188
Minimum0
Maximum883
Zeros51
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2026-01-12T13:59:11.149187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median41
Q3147
95-th percentile385.3
Maximum883
Range883
Interquartile range (IQR)140

Descriptive statistics

Standard deviation137.91917
Coefficient of variation (CV)1.3506673
Kurtosis5.4472393
Mean102.11188
Median Absolute Deviation (MAD)39
Skewness2.120388
Sum147858
Variance19021.699
MonotonicityNot monotonic
2026-01-12T13:59:11.319375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 92
 
1.8%
2 72
 
1.4%
0 51
 
1.0%
3 42
 
0.8%
4 35
 
0.7%
7 28
 
0.6%
5 27
 
0.5%
6 27
 
0.5%
8 25
 
0.5%
90 22
 
0.4%
Other values (359) 1027
 
20.5%
(Missing) 3552
71.0%
ValueCountFrequency (%)
0 51
1.0%
1 92
1.8%
2 72
1.4%
3 42
0.8%
4 35
 
0.7%
5 27
 
0.5%
6 27
 
0.5%
7 28
 
0.6%
8 25
 
0.5%
9 19
 
0.4%
ValueCountFrequency (%)
883 1
< 0.1%
880 1
< 0.1%
876 1
< 0.1%
831 1
< 0.1%
788 1
< 0.1%
786 1
< 0.1%
715 1
< 0.1%
695 1
< 0.1%
694 1
< 0.1%
683 1
< 0.1%

last_repurchase_interval
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct449
Distinct (%)22.0%
Missing2963
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean106.86843
Minimum0
Maximum1344
Zeros626
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2026-01-12T13:59:11.479649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q3169
95-th percentile436
Maximum1344
Range1344
Interquartile range (IQR)169

Descriptive statistics

Standard deviation157.06475
Coefficient of variation (CV)1.4697019
Kurtosis5.0789482
Mean106.86843
Median Absolute Deviation (MAD)25
Skewness2.0166121
Sum217691
Variance24669.334
MonotonicityNot monotonic
2026-01-12T13:59:11.640227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 626
 
12.5%
1 68
 
1.4%
2 47
 
0.9%
3 27
 
0.5%
4 27
 
0.5%
5 20
 
0.4%
7 19
 
0.4%
12 16
 
0.3%
6 16
 
0.3%
8 15
 
0.3%
Other values (439) 1156
 
23.1%
(Missing) 2963
59.3%
ValueCountFrequency (%)
0 626
12.5%
1 68
 
1.4%
2 47
 
0.9%
3 27
 
0.5%
4 27
 
0.5%
5 20
 
0.4%
6 16
 
0.3%
7 19
 
0.4%
8 15
 
0.3%
9 11
 
0.2%
ValueCountFrequency (%)
1344 1
< 0.1%
883 1
< 0.1%
880 1
< 0.1%
876 1
< 0.1%
843 1
< 0.1%
794 1
< 0.1%
790 1
< 0.1%
788 1
< 0.1%
786 1
< 0.1%
776 1
< 0.1%

customer_lifecycle
Real number (ℝ)

HIGH CORRELATION 

Distinct478
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.2
Minimum365
Maximum1776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2026-01-12T13:59:11.781491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum365
5-th percentile365
Q1365
median365
Q3384
95-th percentile691.05
Maximum1776
Range1411
Interquartile range (IQR)19

Descriptive statistics

Standard deviation122.1957
Coefficient of variation (CV)0.29359852
Kurtosis14.270777
Mean416.2
Median Absolute Deviation (MAD)0
Skewness3.3586121
Sum2081000
Variance14931.79
MonotonicityNot monotonic
2026-01-12T13:59:11.925185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 2712
54.2%
366 295
 
5.9%
367 158
 
3.2%
368 95
 
1.9%
369 77
 
1.5%
371 59
 
1.2%
370 58
 
1.2%
372 43
 
0.9%
375 42
 
0.8%
374 32
 
0.6%
Other values (468) 1429
28.6%
ValueCountFrequency (%)
365 2712
54.2%
366 295
 
5.9%
367 158
 
3.2%
368 95
 
1.9%
369 77
 
1.5%
370 58
 
1.2%
371 59
 
1.2%
372 43
 
0.9%
373 32
 
0.6%
374 32
 
0.6%
ValueCountFrequency (%)
1776 1
< 0.1%
1463 1
< 0.1%
1284 1
< 0.1%
1248 1
< 0.1%
1245 1
< 0.1%
1241 1
< 0.1%
1237 1
< 0.1%
1208 1
< 0.1%
1198 1
< 0.1%
1173 1
< 0.1%

cltv
Real number (ℝ)

HIGH CORRELATION 

Distinct555
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.705092
Minimum0.59
Maximum172.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2026-01-12T13:59:12.072334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.59
5-th percentile0.95
Q11
median1
Q32.9
95-th percentile37
Maximum172.08
Range171.49
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation16.029066
Coefficient of variation (CV)2.390581
Kurtosis21.480805
Mean6.705092
Median Absolute Deviation (MAD)0.05
Skewness4.2622216
Sum33525.46
Variance256.93094
MonotonicityNot monotonic
2026-01-12T13:59:12.221904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2321
46.4%
0.95 457
 
9.1%
2 406
 
8.1%
1.9 188
 
3.8%
2.07 89
 
1.8%
13 72
 
1.4%
1.95 66
 
1.3%
3 48
 
1.0%
1.8 40
 
0.8%
4 32
 
0.6%
Other values (545) 1281
25.6%
ValueCountFrequency (%)
0.59 1
 
< 0.1%
0.85 3
 
0.1%
0.9 16
 
0.3%
0.95 457
 
9.1%
1 2321
46.4%
1.02 22
 
0.4%
1.07 5
 
0.1%
1.18 3
 
0.1%
1.51 5
 
0.1%
1.59 3
 
0.1%
ValueCountFrequency (%)
172.08 1
< 0.1%
168 1
< 0.1%
158.2 1
< 0.1%
142.48 1
< 0.1%
127.1 1
< 0.1%
126.8 1
< 0.1%
125.8 1
< 0.1%
125.6 1
< 0.1%
123.4 1
< 0.1%
119.2 1
< 0.1%

first_order_province
Text

MISSING 

Distinct78
Distinct (%)1.6%
Missing119
Missing (%)2.4%
Memory size510.0 KiB
2026-01-12T13:59:12.378786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.0036878
Min length2

Characters and Unicode

Total characters14661
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.5%

Sample

1st row浙江省
2nd row广西壮族自治区
3rd row安徽省
4th row福建省
5th row广东省
ValueCountFrequency (%)
广东省 727
14.9%
江苏省 651
 
13.3%
浙江省 491
 
10.1%
陕西省 222
 
4.5%
北京市 204
 
4.2%
湖南省 202
 
4.1%
上海市 187
 
3.8%
四川省 177
 
3.6%
安徽省 175
 
3.6%
辽宁省 162
 
3.3%
Other values (68) 1683
34.5%
2026-01-12T13:59:12.652746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3756
25.6%
1352
 
9.2%
983
 
6.7%
广 892
 
6.1%
712
 
4.9%
584
 
4.0%
525
 
3.6%
507
 
3.5%
西 442
 
3.0%
421
 
2.9%
Other values (84) 4487
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3756
25.6%
1352
 
9.2%
983
 
6.7%
广 892
 
6.1%
712
 
4.9%
584
 
4.0%
525
 
3.6%
507
 
3.5%
西 442
 
3.0%
421
 
2.9%
Other values (84) 4487
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3756
25.6%
1352
 
9.2%
983
 
6.7%
广 892
 
6.1%
712
 
4.9%
584
 
4.0%
525
 
3.6%
507
 
3.5%
西 442
 
3.0%
421
 
2.9%
Other values (84) 4487
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3756
25.6%
1352
 
9.2%
983
 
6.7%
广 892
 
6.1%
712
 
4.9%
584
 
4.0%
525
 
3.6%
507
 
3.5%
西 442
 
3.0%
421
 
2.9%
Other values (84) 4487
30.6%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size340.3 KiB
North
1840 
South
1755 
West
1263 
Unk
 
142

Length

Max length5
Median length5
Mean length4.6906
Min length3

Characters and Unicode

Total characters23453
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowUnk
3rd rowWest
4th rowNorth
5th rowSouth

Common Values

ValueCountFrequency (%)
North 1840
36.8%
South 1755
35.1%
West 1263
25.3%
Unk 142
 
2.8%

Length

2026-01-12T13:59:12.798594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:12.915561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
north 1840
36.8%
south 1755
35.1%
west 1263
25.3%
unk 142
 
2.8%

Most occurring characters

ValueCountFrequency (%)
t 4858
20.7%
o 3595
15.3%
h 3595
15.3%
N 1840
 
7.8%
r 1840
 
7.8%
S 1755
 
7.5%
u 1755
 
7.5%
W 1263
 
5.4%
e 1263
 
5.4%
s 1263
 
5.4%
Other values (3) 426
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 4858
20.7%
o 3595
15.3%
h 3595
15.3%
N 1840
 
7.8%
r 1840
 
7.8%
S 1755
 
7.5%
u 1755
 
7.5%
W 1263
 
5.4%
e 1263
 
5.4%
s 1263
 
5.4%
Other values (3) 426
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 4858
20.7%
o 3595
15.3%
h 3595
15.3%
N 1840
 
7.8%
r 1840
 
7.8%
S 1755
 
7.5%
u 1755
 
7.5%
W 1263
 
5.4%
e 1263
 
5.4%
s 1263
 
5.4%
Other values (3) 426
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 4858
20.7%
o 3595
15.3%
h 3595
15.3%
N 1840
 
7.8%
r 1840
 
7.8%
S 1755
 
7.5%
u 1755
 
7.5%
W 1263
 
5.4%
e 1263
 
5.4%
s 1263
 
5.4%
Other values (3) 426
 
1.8%

first_order_channel_grp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.3 KiB
Offline
4768 
Online
 
232

Length

Max length7
Median length7
Mean length6.9536
Min length6

Characters and Unicode

Total characters34768
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline
2nd rowOffline
3rd rowOffline
4th rowOffline
5th rowOffline

Common Values

ValueCountFrequency (%)
Offline 4768
95.4%
Online 232
 
4.6%

Length

2026-01-12T13:59:13.029976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:13.130044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
offline 4768
95.4%
online 232
 
4.6%

Most occurring characters

ValueCountFrequency (%)
f 9536
27.4%
n 5232
15.0%
O 5000
14.4%
l 5000
14.4%
i 5000
14.4%
e 5000
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 9536
27.4%
n 5232
15.0%
O 5000
14.4%
l 5000
14.4%
i 5000
14.4%
e 5000
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 9536
27.4%
n 5232
15.0%
O 5000
14.4%
l 5000
14.4%
i 5000
14.4%
e 5000
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 9536
27.4%
n 5232
15.0%
O 5000
14.4%
l 5000
14.4%
i 5000
14.4%
e 5000
14.4%

reg_channel_grp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size350.4 KiB
Offline
3835 
Online
1161 
Others
 
4

Length

Max length7
Median length7
Mean length6.767
Min length6

Characters and Unicode

Total characters33835
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline
2nd rowOffline
3rd rowOffline
4th rowOffline
5th rowOnline

Common Values

ValueCountFrequency (%)
Offline 3835
76.7%
Online 1161
 
23.2%
Others 4
 
0.1%

Length

2026-01-12T13:59:13.231931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T13:59:13.348931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
offline 3835
76.7%
online 1161
 
23.2%
others 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
f 7670
22.7%
n 6157
18.2%
O 5000
14.8%
e 5000
14.8%
l 4996
14.8%
i 4996
14.8%
t 4
 
< 0.1%
h 4
 
< 0.1%
r 4
 
< 0.1%
s 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 7670
22.7%
n 6157
18.2%
O 5000
14.8%
e 5000
14.8%
l 4996
14.8%
i 4996
14.8%
t 4
 
< 0.1%
h 4
 
< 0.1%
r 4
 
< 0.1%
s 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 7670
22.7%
n 6157
18.2%
O 5000
14.8%
e 5000
14.8%
l 4996
14.8%
i 4996
14.8%
t 4
 
< 0.1%
h 4
 
< 0.1%
r 4
 
< 0.1%
s 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 7670
22.7%
n 6157
18.2%
O 5000
14.8%
e 5000
14.8%
l 4996
14.8%
i 4996
14.8%
t 4
 
< 0.1%
h 4
 
< 0.1%
r 4
 
< 0.1%
s 4
 
< 0.1%

Interactions

2026-01-12T13:59:04.180296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:53.965847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:54.903098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:56.836061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:59.449528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.366550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:01.305976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.191502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:03.137028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:04.291060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:54.070737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:55.000685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:57.161290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:59.555176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.505905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:01.405029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.298012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:03.240683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:04.396278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:54.164413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:55.103466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:57.706235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:59.655838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.606771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:01.522567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.408473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:03.344483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:04.506917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:54.273187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:55.202145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:58.051248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:59.759428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.717327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:01.620238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.514110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:03.503888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:04.616550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:54.381672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:55.302232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:58.330516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:59.853897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.810476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:01.718908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.613776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:03.633856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:04.724186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:54.486320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:55.403658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:58.648090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:59.952104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.905691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:01.813909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.713464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:03.762430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:04.818898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:54.578014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:55.542495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:58.941539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.055363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.995738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:01.902168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.808210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:03.853117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:05.148478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:54.689640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:56.221747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:59.212798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.159100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:01.097829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.001835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.915765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:03.963303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:05.311906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:54.794455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:56.528742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:58:59.344081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:00.262741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:01.200578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:02.092097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:03.032192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-12T13:59:04.072659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2026-01-12T13:59:13.447996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
baby_age_first_orderbaby_age_last_orderbaby_residual_lifecyclecltvcustomer_lifecyclefirst_order_channelfirst_order_channel_grpfirst_order_jarfirst_order_province_grpfirst_repurchase_intervalfrisomumgoldlast_repurchase_intervalorder_conversion_jarprestigereg_channelreg_channel_grpstage_f0stage_f1stage_f2stage_f3stage_f4
baby_age_first_order1.0000.839-0.974-0.058-0.0450.0000.0000.0100.040-0.0780.0000.203-0.138-0.3050.2100.2680.0000.0000.1080.0950.5030.108
baby_age_last_order0.8391.000-0.8140.2350.2980.0000.0000.0240.0410.4870.0000.1460.465-0.2650.1510.3030.0000.0000.1200.0280.3740.108
baby_residual_lifecycle-0.974-0.8141.0000.0530.0380.0580.128-0.0110.0610.0650.2400.2200.1290.3140.2640.0960.0820.2400.3980.3790.5050.230
cltv-0.0580.2350.0531.0000.6090.0000.0380.3270.0260.1890.0660.1060.5620.2970.1070.0000.0210.0660.0000.0220.0600.000
customer_lifecycle-0.0450.2980.0380.6091.0000.0470.1000.1050.0150.7670.0000.0460.9240.1140.0480.0650.0370.0000.0230.0590.1320.069
first_order_channel0.0000.0000.0580.0000.0471.0001.000-0.0270.0200.1540.0430.0860.0220.0280.0960.7900.1890.0430.1420.1310.0000.000
first_order_channel_grp0.0000.0000.1280.0380.1001.0001.0000.0370.024-0.1550.0230.000-0.022-0.0240.0160.7530.2630.0230.1420.1310.0000.000
first_order_jar0.0100.024-0.0110.3270.105-0.0270.0371.0000.013-0.0920.3270.0650.0270.5660.1040.0000.0580.3270.0140.0620.1470.000
first_order_province_grp0.0400.0410.0610.0260.0150.0200.0240.0131.000-0.0230.0000.2420.036-0.0200.2470.1820.0770.0000.1200.1190.0330.027
first_repurchase_interval-0.0780.4870.0650.1890.7670.154-0.155-0.092-0.0231.0000.0430.1460.818-0.1320.1690.0600.1070.0430.0970.0720.0460.000
frisomum0.0000.0000.2400.0660.0000.0430.0230.3270.0000.0431.0000.147-0.0580.2360.0760.1630.1570.9930.0730.1320.0000.000
gold0.2030.1460.2200.1060.0460.0860.0000.0650.2420.1460.1471.0000.2020.6220.9710.8530.0880.1470.0630.0500.1870.000
last_repurchase_interval-0.1380.4650.1290.5620.9240.022-0.0220.0270.0360.818-0.0580.2021.0000.1530.0870.0550.1150.0650.0670.0360.0000.000
order_conversion_jar-0.305-0.2650.3140.2970.1140.028-0.0240.566-0.020-0.1320.2360.6220.1531.0000.1030.0000.0510.3160.0000.0720.1610.000
prestige0.2100.1510.2640.1070.0480.0960.0160.1040.2470.1690.0760.9710.0870.1031.0000.8540.0530.0760.0410.0170.1950.000
reg_channel0.2680.3030.0960.0000.0650.7900.7530.0000.1820.0600.1630.8530.0550.0000.8541.0000.9990.1630.1350.1440.2010.000
reg_channel_grp0.0000.0000.0820.0210.0370.1890.2630.0580.0770.1070.1570.0880.1150.0510.0530.9991.0000.1570.0800.0360.0200.016
stage_f00.0000.0000.2400.0660.0000.0430.0230.3270.0000.0430.9930.1470.0650.3160.0760.1630.1571.0000.0730.1320.0000.000
stage_f10.1080.1200.3980.0000.0230.1420.1420.0140.1200.0970.0730.0630.0670.0000.0410.1350.0800.0731.0000.8910.1540.023
stage_f20.0950.0280.3790.0220.0590.1310.1310.0620.1190.0720.1320.0500.0360.0720.0170.1440.0360.1320.8911.0000.2200.038
stage_f30.5030.3740.5050.0600.1320.0000.0000.1470.0330.0460.0000.1870.0000.1610.1950.2010.0200.0000.1540.2201.0000.000
stage_f40.1080.1080.2300.0000.0690.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0160.0000.0230.0380.0001.000

Missing values

2026-01-12T13:59:05.558152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-12T13:59:06.028354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-12T13:59:06.373424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

dac_idreg_channelbaby_residual_lifecyclebaby_age_first_orderbaby_age_last_orderfirst_order_channelfirst_order_jarorder_conversion_jarprestigefrisomumgoldstage_f0stage_f1stage_f2stage_f3stage_f4customer_lifecycle_finished_flagfirst_repurchase_intervallast_repurchase_intervalcustomer_lifecyclecltvfirst_order_provincefirst_order_province_grpfirst_order_channel_grpreg_channel_grp
2308787e290e-d683-11e8-8187-022fee61b306线下1335.04.04.0线下门店11.0000100100YNaNNaN3651.00浙江省SouthOfflineOffline
224045b636287-d684-11e8-8187-022fee61b306CRM系统1250.06.06.0线下门店10.9510000100YNaNNaN3650.95NaNUnkOfflineOffline
23397d2e74f16-d683-11e8-8187-022fee61b306CRM系统1120.011.011.0线下门店10.9510000100Y10.010.03752.75广西壮族自治区WestOfflineOffline
250585faaacca-d684-11e8-8187-022fee61b306线下1409.01.01.0线下门店11.0000101000YNaNNaN3661.00安徽省NorthOfflineOffline
2664bb2c7df4-d684-11e8-8187-022fee61b306品牌微信1536.0-2.0-2.0线下门店11.0000100100YNaNNaN3681.00福建省SouthOfflineOnline
8511127b1090-d684-11e8-8187-022fee61b306线下1380.02.010.0线下门店11.0000100100Y135.0244.060910.00广东省SouthOfflineOffline
5148192eb3e8-d684-11e8-8187-022fee61b306品牌微信1180.09.09.0线下门店10.9010000010YNaN0.03651.80北京市NorthOfflineOnline
779084896107-d683-11e8-8187-022fee61b306线下1367.03.03.0线下门店11.0000101000YNaNNaN3651.00上海市SouthOfflineOffline
11311852f779a-d683-11e8-8187-022fee61b306CRM系统1306.05.05.0线下门店11.0010001000YNaNNaN3651.00江苏省NorthOfflineOffline
190438efc79ac-d683-11e8-8187-022fee61b306线下1444.00.00.0线下门店11.0000100100YNaNNaN3651.00江西省SouthOfflineOffline
dac_idreg_channelbaby_residual_lifecyclebaby_age_first_orderbaby_age_last_orderfirst_order_channelfirst_order_jarorder_conversion_jarprestigefrisomumgoldstage_f0stage_f1stage_f2stage_f3stage_f4customer_lifecycle_finished_flagfirst_repurchase_intervallast_repurchase_intervalcustomer_lifecyclecltvfirst_order_provincefirst_order_province_grpfirst_order_channel_grpreg_channel_grp
123964c726650-d684-11e8-8187-022fee61b306线下1235.07.07.0线下门店11.0000100100YNaNNaN3651.00广东省SouthOfflineOffline
1606767ed9fec-d683-11e8-8187-022fee61b306线下1419.01.01.0线下门店11.0000101000YNaNNaN3661.00江苏省NorthOfflineOffline
396b29045e1-d683-11e8-8187-022fee61b306线下1102.011.011.0线下门店11.0000100100YNaNNaN3651.00浙江省SouthOfflineOffline
5864fc645c90-d683-11e8-8187-022fee61b306线下1375.02.02.0线下门店11.0000101000YNaNNaN3651.00天津市NorthOfflineOffline
97373d485b3b-d684-11e8-8187-022fee61b306品牌微信1185.09.010.0线下门店10.9510000100Y48.048.04131.90浙江省SouthOfflineOnline
6778d769eabc-d683-11e8-8187-022fee61b306CRM系统1315.04.011.0线下门店11.0010001000Y190.0190.05556.10江苏省NorthOfflineOffline
2528488f8458a-d684-11e8-8187-022fee61b306CRM系统1268.06.06.0线下门店10.9510000100YNaNNaN3650.95NaNUnkOfflineOffline
18355e84df59c-d683-11e8-8187-022fee61b306线下1221.07.08.0线下门店11.0000100100Y3.03.036613.00广东省SouthOfflineOffline
2768402112b59-d684-11e8-8187-022fee61b306CRM系统1290.05.05.0线下门店10.9510000100YNaN0.03651.90北京NorthOfflineOffline
411039628a99-d684-11e8-8187-022fee61b306线下1208.08.09.0线下门店33.0000100100Y30.030.039512.00湖南省WestOfflineOffline